from cascadeflow import HarnessConfig

config = HarnessConfig(
    mode="enforce",                     # "off" | "observe" | "enforce"
    verbose=False,                      # Print decisions to stderr

    # Hard constraints
    budget=0.50,                        # Max USD for the run
    max_tool_calls=10,                  # Max tool/function calls
    max_latency_ms=5000.0,              # Max wall-clock ms per call
    max_energy=100.0,                   # Max energy units

    # Soft scoring
    kpi_weights={                       # Relative importance (must sum to ~1.0)
        "quality": 0.6,
        "cost": 0.3,
        "latency": 0.1,
    },
    kpi_targets={"quality": 0.9},       # Target values for KPI dimensions

    # Compliance
    compliance="gdpr",                  # "gdpr" | "hipaa" | "pci" | "strict"
)

import cascadeflow
cascadeflow.init(mode="observe")
# All LLM calls are tracked. Nothing changes.

cascadeflow.init(mode="enforce")

with cascadeflow.run(budget=0.50, compliance="gdpr") as session:
    result = await agent.run("Analyze EU data")
    print(session.summary())

@cascadeflow.agent(
    budget=1.00,
    compliance="hipaa",
    kpi_weights={"quality": 0.8, "cost": 0.2},
)
async def medical_agent(query: str):
    return await llm.complete(query)